Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "78" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459998 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 26.199958 | 0.630285 | -0.350275 | 1.155564 | 1.391443 | 1.098184 | 0.631338 | 0.379703 | 0.4171 | 0.6053 | 0.3675 | nan | nan |
| 2459997 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 28.348047 | 0.651597 | -0.269535 | 1.279756 | 2.122966 | 1.059847 | -0.316610 | 0.940472 | 0.4427 | 0.6299 | 0.3741 | nan | nan |
| 2459996 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 28.955570 | 1.277913 | -0.309347 | 1.485922 | 2.106773 | 1.306237 | 5.620044 | 0.342679 | 0.4340 | 0.6215 | 0.3818 | nan | nan |
| 2459995 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 31.102708 | 0.840537 | -0.468175 | 1.451715 | 1.551493 | 0.938631 | 3.089761 | 0.125543 | 0.4424 | 0.6260 | 0.3706 | nan | nan |
| 2459994 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 32.158663 | 0.510469 | -0.352954 | 1.251250 | 1.855335 | 0.708492 | 0.801225 | -0.005638 | 0.4343 | 0.6198 | 0.3675 | nan | nan |
| 2459993 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 37.398001 | 1.167525 | -0.271492 | 1.480329 | 2.692613 | 0.763709 | 1.775302 | -0.165422 | 0.4090 | 0.6303 | 0.3921 | nan | nan |
| 2459991 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 38.742808 | 0.775794 | -0.302513 | 1.484790 | 2.303732 | 0.939636 | 2.741577 | -0.001108 | 0.4248 | 0.6131 | 0.3777 | nan | nan |
| 2459990 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 32.315102 | 0.675036 | -0.301208 | 1.567483 | 2.253765 | 1.131056 | 2.111224 | 0.383468 | 0.4215 | 0.6160 | 0.3779 | nan | nan |
| 2459989 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 32.505523 | 0.594529 | -0.162183 | 1.360455 | 1.870929 | 0.521112 | 1.204717 | -0.122559 | 0.4202 | 0.6163 | 0.3800 | nan | nan |
| 2459988 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 38.328967 | 0.927564 | -0.374318 | 1.618267 | 3.082994 | 1.709353 | 2.943008 | 0.642266 | 0.4148 | 0.6049 | 0.3664 | nan | nan |
| 2459987 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 30.644557 | 0.647038 | -0.377908 | 1.278273 | 1.554610 | 0.563786 | -0.179595 | 0.287633 | 0.4364 | 0.6248 | 0.3681 | nan | nan |
| 2459986 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 37.231917 | 1.013579 | -0.424285 | 1.524819 | 2.259761 | 0.146277 | 2.585436 | 1.645165 | 0.4602 | 0.6398 | 0.3358 | nan | nan |
| 2459985 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 34.731824 | 0.982230 | -0.457262 | 1.236438 | 1.588198 | 0.238647 | 3.642595 | 0.634328 | 0.4312 | 0.6203 | 0.3742 | nan | nan |
| 2459984 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 31.843441 | 0.795673 | -0.391064 | 1.313570 | 3.404819 | 1.774430 | 2.104639 | 1.327276 | 0.4523 | 0.6309 | 0.3512 | nan | nan |
| 2459983 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 32.841434 | 0.017107 | -0.380738 | 1.174815 | 2.457828 | 0.372592 | 2.428701 | 0.561796 | 0.4802 | 0.6559 | 0.3154 | nan | nan |
| 2459982 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 15.949253 | -0.100663 | -0.826031 | 0.430399 | -0.035171 | -0.923640 | 0.071474 | -0.516982 | 0.5253 | 0.6753 | 0.2987 | nan | nan |
| 2459981 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 31.141957 | -0.235663 | -0.381827 | 1.374338 | 2.755279 | 0.301092 | 3.061327 | 1.239264 | 0.4245 | 0.6160 | 0.3738 | nan | nan |
| 2459980 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 28.294977 | -0.348593 | -0.590671 | 0.778548 | 2.256429 | -0.329769 | 1.414681 | 0.229916 | 0.4916 | 0.6567 | 0.3180 | nan | nan |
| 2459979 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 32.362634 | -0.488438 | -0.516431 | 0.778595 | 2.137016 | -0.525827 | 1.397140 | 0.721322 | 0.4196 | 0.6146 | 0.3758 | nan | nan |
| 2459978 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 32.415981 | -0.367226 | -0.458810 | 1.021382 | 2.316053 | 0.218761 | 2.198807 | 1.975215 | 0.4145 | 0.6091 | 0.3805 | nan | nan |
| 2459977 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 31.547309 | -0.118651 | -0.496014 | 0.766587 | 3.697786 | 0.132005 | 3.775022 | 2.928838 | 0.3992 | 0.5786 | 0.3431 | nan | nan |
| 2459976 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 31.884495 | -0.242156 | -0.515729 | 0.953585 | 1.893809 | -0.041402 | 0.628457 | 0.399104 | 0.4292 | 0.6222 | 0.3766 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 26.199958 | 26.199958 | 0.630285 | -0.350275 | 1.155564 | 1.391443 | 1.098184 | 0.631338 | 0.379703 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 28.348047 | 28.348047 | 0.651597 | -0.269535 | 1.279756 | 2.122966 | 1.059847 | -0.316610 | 0.940472 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 28.955570 | 28.955570 | 1.277913 | -0.309347 | 1.485922 | 2.106773 | 1.306237 | 5.620044 | 0.342679 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 31.102708 | 31.102708 | 0.840537 | -0.468175 | 1.451715 | 1.551493 | 0.938631 | 3.089761 | 0.125543 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 32.158663 | 32.158663 | 0.510469 | -0.352954 | 1.251250 | 1.855335 | 0.708492 | 0.801225 | -0.005638 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 37.398001 | 37.398001 | 1.167525 | -0.271492 | 1.480329 | 2.692613 | 0.763709 | 1.775302 | -0.165422 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 38.742808 | 38.742808 | 0.775794 | -0.302513 | 1.484790 | 2.303732 | 0.939636 | 2.741577 | -0.001108 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 32.315102 | 0.675036 | 32.315102 | 1.567483 | -0.301208 | 1.131056 | 2.253765 | 0.383468 | 2.111224 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 32.505523 | 0.594529 | 32.505523 | 1.360455 | -0.162183 | 0.521112 | 1.870929 | -0.122559 | 1.204717 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 38.328967 | 0.927564 | 38.328967 | 1.618267 | -0.374318 | 1.709353 | 3.082994 | 0.642266 | 2.943008 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 30.644557 | 30.644557 | 0.647038 | -0.377908 | 1.278273 | 1.554610 | 0.563786 | -0.179595 | 0.287633 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 37.231917 | 1.013579 | 37.231917 | 1.524819 | -0.424285 | 0.146277 | 2.259761 | 1.645165 | 2.585436 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 34.731824 | 0.982230 | 34.731824 | 1.236438 | -0.457262 | 0.238647 | 1.588198 | 0.634328 | 3.642595 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 31.843441 | 31.843441 | 0.795673 | -0.391064 | 1.313570 | 3.404819 | 1.774430 | 2.104639 | 1.327276 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 32.841434 | 32.841434 | 0.017107 | -0.380738 | 1.174815 | 2.457828 | 0.372592 | 2.428701 | 0.561796 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 15.949253 | 15.949253 | -0.100663 | -0.826031 | 0.430399 | -0.035171 | -0.923640 | 0.071474 | -0.516982 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 31.141957 | -0.235663 | 31.141957 | 1.374338 | -0.381827 | 0.301092 | 2.755279 | 1.239264 | 3.061327 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 28.294977 | -0.348593 | 28.294977 | 0.778548 | -0.590671 | -0.329769 | 2.256429 | 0.229916 | 1.414681 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 32.362634 | 32.362634 | -0.488438 | -0.516431 | 0.778595 | 2.137016 | -0.525827 | 1.397140 | 0.721322 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 32.415981 | -0.367226 | 32.415981 | 1.021382 | -0.458810 | 0.218761 | 2.316053 | 1.975215 | 2.198807 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 31.547309 | 31.547309 | -0.118651 | -0.496014 | 0.766587 | 3.697786 | 0.132005 | 3.775022 | 2.928838 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | N06 | not_connected | ee Shape | 31.884495 | -0.242156 | 31.884495 | 0.953585 | -0.515729 | -0.041402 | 1.893809 | 0.399104 | 0.628457 |